{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM用法速查表\n",
    "### by 寒小阳"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.读取csv数据并指定参数建模\n",
    "\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load data...\n",
      "开始训练...\n",
      "[1]\tvalid_0's l2: 0.24288\tvalid_0's auc: 0.764496\n",
      "Training until validation scores don't improve for 5 rounds.\n",
      "[2]\tvalid_0's l2: 0.239307\tvalid_0's auc: 0.766173\n",
      "[3]\tvalid_0's l2: 0.235559\tvalid_0's auc: 0.785547\n",
      "[4]\tvalid_0's l2: 0.230771\tvalid_0's auc: 0.797786\n",
      "[5]\tvalid_0's l2: 0.226297\tvalid_0's auc: 0.805155\n",
      "[6]\tvalid_0's l2: 0.223692\tvalid_0's auc: 0.800979\n",
      "[7]\tvalid_0's l2: 0.220941\tvalid_0's auc: 0.806566\n",
      "[8]\tvalid_0's l2: 0.217982\tvalid_0's auc: 0.808566\n",
      "[9]\tvalid_0's l2: 0.215351\tvalid_0's auc: 0.809041\n",
      "[10]\tvalid_0's l2: 0.213064\tvalid_0's auc: 0.805953\n",
      "[11]\tvalid_0's l2: 0.211053\tvalid_0's auc: 0.804631\n",
      "[12]\tvalid_0's l2: 0.209336\tvalid_0's auc: 0.802922\n",
      "[13]\tvalid_0's l2: 0.207492\tvalid_0's auc: 0.802011\n",
      "[14]\tvalid_0's l2: 0.206016\tvalid_0's auc: 0.80193\n",
      "Early stopping, best iteration is:\n",
      "[9]\tvalid_0's l2: 0.215351\tvalid_0's auc: 0.809041\n",
      "保存模型...\n",
      "开始预测...\n",
      "预估结果的rmse为:\n",
      "0.4640593794679212\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import json\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "# 加载数据集合\n",
    "print('Load data...')\n",
    "df_train = pd.read_csv('./data/regression.train.txt', header=None, sep='\\t')\n",
    "df_test = pd.read_csv('./data/regression.test.txt', header=None, sep='\\t')\n",
    "\n",
    "# 设定训练集和测试集\n",
    "y_train = df_train[0].values\n",
    "y_test = df_test[0].values\n",
    "X_train = df_train.drop(0, axis=1).values\n",
    "X_test = df_test.drop(0, axis=1).values\n",
    "\n",
    "# 构建lgb中的Dataset格式\n",
    "lgb_train = lgb.Dataset(X_train, y_train)\n",
    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
    "\n",
    "# 敲定好一组参数\n",
    "params = {\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'regression',\n",
    "    'metric': {'l2', 'auc'},\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.05,\n",
    "    'feature_fraction': 0.9,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'bagging_freq': 5,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "print('开始训练...')\n",
    "# 训练\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=20,\n",
    "                valid_sets=lgb_eval,\n",
    "                early_stopping_rounds=5)\n",
    "\n",
    "# 保存模型\n",
    "print('保存模型...')\n",
    "# 保存模型到文件中\n",
    "gbm.save_model('model.txt')\n",
    "\n",
    "print('开始预测...')\n",
    "# 预测\n",
    "y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)\n",
    "# 评估\n",
    "print('预估结果的rmse为:')\n",
    "print(mean_squared_error(y_test, y_pred) ** 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.添加样本权重训练\n",
    "\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载数据...\n",
      "开始训练...\n",
      "[1]\ttraining's binary_logloss: 0.68205\n",
      "[2]\ttraining's binary_logloss: 0.673618\n",
      "[3]\ttraining's binary_logloss: 0.665891\n",
      "[4]\ttraining's binary_logloss: 0.656874\n",
      "[5]\ttraining's binary_logloss: 0.648523\n",
      "[6]\ttraining's binary_logloss: 0.641874\n",
      "[7]\ttraining's binary_logloss: 0.636029\n",
      "[8]\ttraining's binary_logloss: 0.629427\n",
      "[9]\ttraining's binary_logloss: 0.623354\n",
      "[10]\ttraining's binary_logloss: 0.617593\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import json\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 加载数据集\n",
    "print('加载数据...')\n",
    "df_train = pd.read_csv('./data/binary.train', header=None, sep='\\t')\n",
    "df_test = pd.read_csv('./data/binary.test', header=None, sep='\\t')\n",
    "W_train = pd.read_csv('./data/binary.train.weight', header=None)[0]\n",
    "W_test = pd.read_csv('./data/binary.test.weight', header=None)[0]\n",
    "\n",
    "y_train = df_train[0].values\n",
    "y_test = df_test[0].values\n",
    "X_train = df_train.drop(0, axis=1).values\n",
    "X_test = df_test.drop(0, axis=1).values\n",
    "\n",
    "num_train, num_feature = X_train.shape\n",
    "\n",
    "# 加载数据的同时加载权重\n",
    "lgb_train = lgb.Dataset(X_train, y_train,\n",
    "                        weight=W_train, free_raw_data=False)\n",
    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,\n",
    "                       weight=W_test, free_raw_data=False)\n",
    "\n",
    "# 设定参数\n",
    "params = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'binary_logloss',\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.05,\n",
    "    'feature_fraction': 0.9,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'bagging_freq': 5,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# 产出特征名称\n",
    "feature_name = ['feature_' + str(col) for col in range(num_feature)]\n",
    "\n",
    "print('开始训练...')\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=10,\n",
    "                valid_sets=lgb_train,  # 评估训练集\n",
    "                feature_name=feature_name,\n",
    "                categorical_feature=[21])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.模型的载入与预测\n",
    "\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "完成10轮训练...\n",
      "第7个特征为:\n",
      "'feature_6'\n",
      "特征名称:\n",
      "[u'feature_0', u'feature_1', u'feature_2', u'feature_3', u'feature_4', u'feature_5', u'feature_6', u'feature_7', u'feature_8', u'feature_9', u'feature_10', u'feature_11', u'feature_12', u'feature_13', u'feature_14', u'feature_15', u'feature_16', u'feature_17', u'feature_18', u'feature_19', u'feature_20', u'feature_21', u'feature_22', u'feature_23', u'feature_24', u'feature_25', u'feature_26', u'feature_27']\n",
      "特征重要度:\n",
      "[8, 5, 1, 19, 7, 33, 2, 0, 2, 10, 5, 2, 0, 9, 3, 3, 0, 2, 2, 5, 1, 0, 36, 3, 33, 45, 29, 35]\n",
      "加载模型用于预测\n",
      "在测试集上的rmse为:\n",
      "0.4629245607636925\n"
     ]
    }
   ],
   "source": [
    "# 查看特征名称\n",
    "print('完成10轮训练...')\n",
    "print('第7个特征为:')\n",
    "print(repr(lgb_train.feature_name[6]))\n",
    "\n",
    "# 存储模型\n",
    "gbm.save_model('./model/lgb_model.txt')\n",
    "\n",
    "# 特征名称\n",
    "print('特征名称:')\n",
    "print(gbm.feature_name())\n",
    "\n",
    "# 特征重要度\n",
    "print('特征重要度:')\n",
    "print(list(gbm.feature_importance()))\n",
    "\n",
    "# 加载模型\n",
    "print('加载模型用于预测')\n",
    "bst = lgb.Booster(model_file='./model/lgb_model.txt')\n",
    "# 预测\n",
    "y_pred = bst.predict(X_test)\n",
    "# 在测试集评估效果\n",
    "print('在测试集上的rmse为:')\n",
    "print(mean_squared_error(y_test, y_pred) ** 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.接着之前的模型继续训练\n",
    "\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11]\tvalid_0's binary_logloss: 0.616177\n",
      "[12]\tvalid_0's binary_logloss: 0.611792\n",
      "[13]\tvalid_0's binary_logloss: 0.607043\n",
      "[14]\tvalid_0's binary_logloss: 0.602314\n",
      "[15]\tvalid_0's binary_logloss: 0.598433\n",
      "[16]\tvalid_0's binary_logloss: 0.595238\n",
      "[17]\tvalid_0's binary_logloss: 0.592047\n",
      "[18]\tvalid_0's binary_logloss: 0.588673\n",
      "[19]\tvalid_0's binary_logloss: 0.586084\n",
      "[20]\tvalid_0's binary_logloss: 0.584033\n",
      "以旧模型为初始化，完成第 10-20 轮训练...\n",
      "[21]\tvalid_0's binary_logloss: 0.616177\n",
      "[22]\tvalid_0's binary_logloss: 0.611834\n",
      "[23]\tvalid_0's binary_logloss: 0.607177\n",
      "[24]\tvalid_0's binary_logloss: 0.602577\n",
      "[25]\tvalid_0's binary_logloss: 0.59831\n",
      "[26]\tvalid_0's binary_logloss: 0.595259\n",
      "[27]\tvalid_0's binary_logloss: 0.592201\n",
      "[28]\tvalid_0's binary_logloss: 0.589017\n",
      "[29]\tvalid_0's binary_logloss: 0.586597\n",
      "[30]\tvalid_0's binary_logloss: 0.584454\n",
      "逐步调整学习率完成第 20-30 轮训练...\n",
      "[31]\tvalid_0's binary_logloss: 0.616053\n",
      "[32]\tvalid_0's binary_logloss: 0.612291\n",
      "[33]\tvalid_0's binary_logloss: 0.60856\n",
      "[34]\tvalid_0's binary_logloss: 0.605387\n",
      "[35]\tvalid_0's binary_logloss: 0.601744\n",
      "[36]\tvalid_0's binary_logloss: 0.598556\n",
      "[37]\tvalid_0's binary_logloss: 0.595585\n",
      "[38]\tvalid_0's binary_logloss: 0.593228\n",
      "[39]\tvalid_0's binary_logloss: 0.59018\n",
      "[40]\tvalid_0's binary_logloss: 0.588391\n",
      "逐步调整bagging比率完成第 30-40 轮训练...\n"
     ]
    }
   ],
   "source": [
    "# 继续训练\n",
    "# 从./model/model.txt中加载模型初始化\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=10,\n",
    "                init_model='./model/lgb_model.txt',\n",
    "                valid_sets=lgb_eval)\n",
    "\n",
    "print('以旧模型为初始化，完成第 10-20 轮训练...')\n",
    "\n",
    "# 在训练的过程中调整超参数\n",
    "# 比如这里调整的是学习率\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=10,\n",
    "                init_model=gbm,\n",
    "                learning_rates=lambda iter: 0.05 * (0.99 ** iter),\n",
    "                valid_sets=lgb_eval)\n",
    "\n",
    "print('逐步调整学习率完成第 20-30 轮训练...')\n",
    "\n",
    "# 调整其他超参数\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=10,\n",
    "                init_model=gbm,\n",
    "                valid_sets=lgb_eval,\n",
    "                callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)])\n",
    "\n",
    "print('逐步调整bagging比率完成第 30-40 轮训练...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 5.自定义损失函数\n",
    "\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[41]\tvalid_0's binary_logloss: 0.614429\tvalid_0's error: 0.268\n",
      "[42]\tvalid_0's binary_logloss: 0.610689\tvalid_0's error: 0.26\n",
      "[43]\tvalid_0's binary_logloss: 0.606267\tvalid_0's error: 0.264\n",
      "[44]\tvalid_0's binary_logloss: 0.601949\tvalid_0's error: 0.258\n",
      "[45]\tvalid_0's binary_logloss: 0.597271\tvalid_0's error: 0.266\n",
      "[46]\tvalid_0's binary_logloss: 0.593971\tvalid_0's error: 0.276\n",
      "[47]\tvalid_0's binary_logloss: 0.591427\tvalid_0's error: 0.278\n",
      "[48]\tvalid_0's binary_logloss: 0.588301\tvalid_0's error: 0.284\n",
      "[49]\tvalid_0's binary_logloss: 0.586562\tvalid_0's error: 0.288\n",
      "[50]\tvalid_0's binary_logloss: 0.584056\tvalid_0's error: 0.288\n",
      "用自定义的损失函数与评估标准完成第40-50轮...\n"
     ]
    }
   ],
   "source": [
    "# 类似在xgboost中的形式\n",
    "# 自定义损失函数需要\n",
    "def loglikelood(preds, train_data):\n",
    "    labels = train_data.get_label()\n",
    "    preds = 1. / (1. + np.exp(-preds))\n",
    "    grad = preds - labels\n",
    "    hess = preds * (1. - preds)\n",
    "    return grad, hess\n",
    "\n",
    "\n",
    "# 自定义评估函数\n",
    "def binary_error(preds, train_data):\n",
    "    labels = train_data.get_label()\n",
    "    return 'error', np.mean(labels != (preds > 0.5)), False\n",
    "\n",
    "\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=10,\n",
    "                init_model=gbm,\n",
    "                fobj=loglikelood,\n",
    "                feval=binary_error,\n",
    "                valid_sets=lgb_eval)\n",
    "\n",
    "print('用自定义的损失函数与评估标准完成第40-50轮...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn与LightGBM配合使用\n",
    "#### 1.LightGBM建模，sklearn评估\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载数据...\n",
      "开始训练...\n",
      "[1]\tvalid_0's l1: 0.491735\n",
      "Training until validation scores don't improve for 5 rounds.\n",
      "[2]\tvalid_0's l1: 0.486563\n",
      "[3]\tvalid_0's l1: 0.481489\n",
      "[4]\tvalid_0's l1: 0.476848\n",
      "[5]\tvalid_0's l1: 0.47305\n",
      "[6]\tvalid_0's l1: 0.469049\n",
      "[7]\tvalid_0's l1: 0.465556\n",
      "[8]\tvalid_0's l1: 0.462208\n",
      "[9]\tvalid_0's l1: 0.458676\n",
      "[10]\tvalid_0's l1: 0.454998\n",
      "[11]\tvalid_0's l1: 0.452047\n",
      "[12]\tvalid_0's l1: 0.449158\n",
      "[13]\tvalid_0's l1: 0.44608\n",
      "[14]\tvalid_0's l1: 0.443554\n",
      "[15]\tvalid_0's l1: 0.440643\n",
      "[16]\tvalid_0's l1: 0.437687\n",
      "[17]\tvalid_0's l1: 0.435454\n",
      "[18]\tvalid_0's l1: 0.433288\n",
      "[19]\tvalid_0's l1: 0.431297\n",
      "[20]\tvalid_0's l1: 0.428946\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20]\tvalid_0's l1: 0.428946\n",
      "开始预测...\n",
      "预测结果的rmse是:\n",
      "0.4441153344254208\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 加载数据\n",
    "print('加载数据...')\n",
    "df_train = pd.read_csv('./data/regression.train.txt', header=None, sep='\\t')\n",
    "df_test = pd.read_csv('./data/regression.test.txt', header=None, sep='\\t')\n",
    "\n",
    "# 取出特征和标签\n",
    "y_train = df_train[0].values\n",
    "y_test = df_test[0].values\n",
    "X_train = df_train.drop(0, axis=1).values\n",
    "X_test = df_test.drop(0, axis=1).values\n",
    "\n",
    "print('开始训练...')\n",
    "# 直接初始化LGBMRegressor\n",
    "# 这个LightGBM的Regressor和sklearn中其他Regressor基本是一致的\n",
    "gbm = lgb.LGBMRegressor(objective='regression',\n",
    "                        num_leaves=31,\n",
    "                        learning_rate=0.05,\n",
    "                        n_estimators=20)\n",
    "\n",
    "# 使用fit函数拟合\n",
    "gbm.fit(X_train, y_train,\n",
    "        eval_set=[(X_test, y_test)],\n",
    "        eval_metric='l1',\n",
    "        early_stopping_rounds=5)\n",
    "\n",
    "# 预测\n",
    "print('开始预测...')\n",
    "y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)\n",
    "# 评估预测结果\n",
    "print('预测结果的rmse是:')\n",
    "print(mean_squared_error(y_test, y_pred) ** 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.网格搜索查找最优超参数\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "用网格搜索找到的最优超参数为:\n",
      "{'n_estimators': 40, 'learning_rate': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# 配合scikit-learn的网格搜索交叉验证选择最优超参数\n",
    "estimator = lgb.LGBMRegressor(num_leaves=31)\n",
    "\n",
    "param_grid = {\n",
    "    'learning_rate': [0.01, 0.1, 1],\n",
    "    'n_estimators': [20, 40]\n",
    "}\n",
    "\n",
    "gbm = GridSearchCV(estimator, param_grid)\n",
    "\n",
    "gbm.fit(X_train, y_train)\n",
    "\n",
    "print('用网格搜索找到的最优超参数为:')\n",
    "print(gbm.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.绘图解释\n",
    "**by 寒小阳**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载数据...\n",
      "开始训练...\n",
      "[10]\ttraining's l2: 0.217995\ttraining's l1: 0.457448\tvalid_1's l2: 0.21641\tvalid_1's l1: 0.456464\n",
      "[20]\ttraining's l2: 0.205099\ttraining's l1: 0.436869\tvalid_1's l2: 0.201616\tvalid_1's l1: 0.434057\n",
      "[30]\ttraining's l2: 0.197421\ttraining's l1: 0.421302\tvalid_1's l2: 0.192514\tvalid_1's l1: 0.417019\n",
      "[40]\ttraining's l2: 0.192856\ttraining's l1: 0.411107\tvalid_1's l2: 0.187258\tvalid_1's l1: 0.406303\n",
      "[50]\ttraining's l2: 0.189593\ttraining's l1: 0.403695\tvalid_1's l2: 0.183688\tvalid_1's l1: 0.398997\n",
      "[60]\ttraining's l2: 0.187043\ttraining's l1: 0.398704\tvalid_1's l2: 0.181009\tvalid_1's l1: 0.393977\n",
      "[70]\ttraining's l2: 0.184982\ttraining's l1: 0.394876\tvalid_1's l2: 0.178803\tvalid_1's l1: 0.389805\n",
      "[80]\ttraining's l2: 0.1828\ttraining's l1: 0.391147\tvalid_1's l2: 0.176799\tvalid_1's l1: 0.386476\n",
      "[90]\ttraining's l2: 0.180817\ttraining's l1: 0.388101\tvalid_1's l2: 0.175775\tvalid_1's l1: 0.384404\n",
      "[100]\ttraining's l2: 0.179171\ttraining's l1: 0.385174\tvalid_1's l2: 0.175321\tvalid_1's l1: 0.382929\n",
      "在训练过程中绘图...\n"
     ]
    },
    {
     "data": {
      "image/png": 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RSeviwjkiWWQBgbb9QNvr6sYDccClQJKI3A/0Ae4FlgFLReS/xpgDVU8yxrwNvA2QlJRk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\nqTr9f+DIUYUjSKUkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84f1cbcf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "画出特征重要度...\n"
     ]
    },
    {
     "data": {
      "image/png": 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YACyqwf7Ncu/ggw/mnnvu2WXZyJEjueOOOzKqyBpJZpnFEfHvwL9XY/9mZlY+f6HMzKzg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84ecb569d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "画出第84颗树...\n"
     ]
    },
    {
     "data": {
      "image/png": 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YExkn2Iw1pStXruCPP/7A+++/L3YoLdq5c+cwcOBA3Lp1C126dBE7HMYYY+xx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\nmntKsNXr9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sGnn36Kmzdvih1OtxcfHw+lUokdO3aIHQpjPQEn2Iwxxjregw8+iBMnTqC0\ntBR2dnZih9Mj+Pr64s0339Q5BIcxZlJGJdj86C7GGGMmcenSJdja2uLAgQOor6/n5LoDpaenIzg4\nGEFBQfjiiy/EDoexHo8TbMYYY21y6tQpBAUFYf/+/aiurhbGKbOOFRUVhatXr2L06NGwtbVFfX29\n2CEx1mNxgs0YY+yeVVVVISYmBn379sXSpUvFDocBCA0NxbvvvouhQ4ciMTFR7HAY65F4DDZjjLFW\nUyqVWLZsGd9c14mpbzb97LPPkJqaKnY4jHUXfJMjY4wx05s2bRrs7e2xcuVKODs7ix0OM4Krqyus\nrKyQnZ0tdiiMdXWcYDPGGDOdtLQ0BAQE8ANOujAfHx98+OGHmD17ttihMNZV8SwijDHGTOOLL75A\nSEgITp8+zcl1F3b58mXMnTsXmZmZYofCWLfGCTZjjDG96uvrYW1tjYceeggVFRUYOnSo2CGxNpDJ\nZGhsbMT/+3//DxYWFrh+/brYITHWLXGCzRhjrJmqqiosW7YMkydPRm1tLYKCgsQOiZnQihUr0NDQ\ngN27dyM8PFzscBjrdngMNmOMMS3x8fHIycnBmjVr4OXlJXY4rJ0REWQyGTw8PHDjxg2xw2Gss+Mx\n2Iwxxlpn79692LNnD/bu3cvJdQ8hkUhw6dIl9O3bF/Hx8WKHw1i3wAk2Y4wxbNy4ES4uLrC1tUVu\nbq7Y4bAO5ufnh4MHD+L999+HRCJBUVGR2CEx1qVxgs0YYz2YSqWCg4MDAgMDUVRUhLi4OLFDYiJy\nd3cHEeGNN96AmZkZsrKyxA6JsS6JE2zGGOuB6uvrYWNjg+joaJSXl+P+++9vtk5GRgZCQkJEiI6J\nbc2aNVCpVFi9ejViY2PbNDY7JCQEGRkZJoyOsc6PE2zGGOuBwsPD8d577+HEiRPNyoqLixETEwM/\nPz94eHiIEB3rLFasWIHy8nKEhYWhsbHxntrw8PCAn58f1qxZY+LoGOu8OMFmjLEeJCEhAXK5HJcv\nX8bSpUthbm4OADh48CBmz54NiUSCzz//HAkJCVCpVPj1119FjpiJLSkpCVVVVYiKisKyZctaXf/X\nX3+FSqXC6NGj0a9fP0gkEsyePfueE3bGugJOsBljrAfYu3cvPD09kZ+fD6VSCYlEe6aphx9+GEeO\nHEFiYiI++OADkaJkndnvv/+O+Ph4mJub39NsIwEBAbh16xbOnTuHI0eO8Cw1rFvjBJsxxro5hUKB\nyZMn48qVK5g+fbrOdfbv348HH3wQkZGRehNsiUSi94f1DH379sW2bdvw448/4vDhw62qe+vWLQQG\nBuK+++7Dgw8+iJycnHaKkjHxcYLNGGPd1O3bt/HQQw/h7bffBhHBxcVF77oPP/wwNm3aBCLCCy+8\ngFGjRsHMzAxjx44V1iEivT+s55gxYwYKCwtRXV0NT0/PFtcfO3YszMzMcOjQIVy7dg1EhE2bNgnD\nkxjrjvhJjowx1g2NGDECsbGxeO+992BtbS12OKwbGzhwIAoKClBcXMzfZrCewKg/ck6wGWOsG4mM\njMTVq1dRXl7OVwhZhwoODsbs2bOxdOlSsUNhrD0ZlWBbtHcUjDHGOsayZcsglUpx4cIFTq5Zh7tw\n4QKkUikmTZqEQYMGiR0OY6LiMdiMMdbFlZaWwszMDC+++CKOHz+OgQMHih0S64EsLS1RW1uLkydP\nwsHBAWfPnhU7JMZEwwk2Y4x1UQqFAhKJBPPmzYNKpUKfPn3EDokxzJ8/H+Xl5bh27Rr69euHI0eO\niB0SYx2Ox2AzxlgX9PDDD8PX15efjsc6vb179yI+Pp6n5WPdBd/kyBhj3Q0R4ZtvvsFnn32G1NRU\nscNhzCjx8fFQKpXYsWOH2KEw1lacYDPGWHfyj3/8A9u2bcO6desQEREhdjiMtUpVVRUcHR0xZswY\n7N+/X+xwGLtXnGAzxlh3UF1dDQcHByQnJyMgIEDscBhrk/r6emG2m6CgILHDYay1jEqw+SZHxhjr\npEpLS2Fubo4ZM2agoaGBk2vWLahnG/n1118RGhqKP//8U+yQGDM5voLNGGOdlI+PDz788EPMnj1b\n7FAYaxd/+ctfkJCQgJKSEtjb24sdDmPG4CvYjDHW2c2aNavZa66urujduzcyMjI4uWbd2pEjR1Bf\nX49Zs2YhPj5eq6y4uBgTJkwQKTLG2oYTbMYYE8lPP/2E7777DnPmzAEArF27FgMGDEBBQQGys7NF\njo6xjrNz506sWLECEokEjz32GACgV69e+OWXXzBp0iSRo2Os9XiICGOMicTe3h5VVVUAgPXr12Pe\nvHkoLy+HjY2NyJExJo79+/djwYIFePXVV/G3v/0NjY2NMDMzQ2Njo9ihMabGs4gwxlhnNXPmTGze\nvFlYtrS0RF1dnYgRMdZ5mJubQ6VSCcvOzs4oKioSMSLGBDwGmzHGOiNLS0ut5Bq4O3VZv379RIqI\nsc7jrbfe0kqugbvjsZcsWSJSRIy1HifYjDHWgUaPHo2GhgZhWSK5ezHE1tYWzz33nFhhMdYpqFQq\n9O/fH56enjAzM4OlpaVQtnLlStjZ2YkYHWPG4yEijLEuRalUCv8qlUo0NjairKxM73pq+tYDALlc\n3uJr5ubmcHR0FP5Vl+uqa4j6q29zc3M0NjZiwoQJeOeddxAeHg4LC4tWtcVYd7Zp0yYkJCTg22+/\nhbW1NWpra2Fubo6SkhJIpVKj26mtrUVVVRUqKipQX18v9B1NlZeXa538As37ETU7OztYW1trvWao\nH5HL5ZDL5TrrsS6Hx2AzxtqfQqFAZmYm7ty5g7KyMmRkZCAzMxMZGRkoKSlBUVERFAqFcDOfJjs7\nO3h4eEAul0MqlcLNzQ1yuRw2NjawtbWFg4MDLCwsIJfLYWFhAQcHh2YfWACExFdTaxPfppp+sFZX\nV6Ompgb19fWoqKholujX1dWhsrISVVVVqK2thVKphEqlQmFhIRQKBerq6pCeni6cFDTl5OQEZ2dn\nuLu7w8fHBzKZDL6+vvD29oZMJoO3tzf8/PzavF+MdQaFhYVIT09Heno6SktLkZ6ejoyMDKHfKCws\nRF5eHmpra5vVtbGxgZeXF1xcXCCTyYS+QCKRwMnJCXK5HPb29rCysoKTkxOsrKwglUqF5Fbd32he\nHQcAa2vrNl0hb9pnqPsKlUqF0tJSKJVKrSRf3WdUVlairq4OSqUS9fX1wr4XFRWhsrISCoWi2bbU\nsbq6usLb21voM3x8fODj4wM/Pz9h2crK6p73ienECTZjzDCVSoX8/Hxcv34d165dw/Xr11FUVCQs\nl5aWCuv2798fAwYMEBI/dYfu7e0tJH/q4Q7MtGpra4WTmMzMTK0TmdTUVBQWFiIrKwvA3SvkvXr1\nwsCBAzFw4EC4uroiICAAAQEBGDRoEGQymch7w7qD2tpaXLlyRavfSElJwfXr15Gbmyus16dPH/Tq\n1QsDBgwQkj914ufr6ws/Pz+YmfFoVVNSqVS4c+cOSktLhZMW9YnMnTt3cO3aNRQVFaG6uhoA4Onp\nCRcXFwQGBiIoKAgBAQFwcXHBoEGD4OPjI/LedEqcYDPWE2VlZSE9PR1//vknkpKSkJSUJFwNAYDg\n4GB4enoiPDwc4eHh6N27NwICAuDu7i5y5Kw9ZWVl4fLly8jPzxf+LpKSkoRvFry8vLT+LkaMGAEf\nHx+4uLiIHDnrCCqVCrm5uThx4gQSExOFfiMlJQXA3XsFQkNDERERgYiICPTp0wfBwcHo37+/yJGz\n9lBWVoaUlBQkJSUhMTERCoUCiYmJyMvLAwDIZDKtv4WhQ4dixIgRPeVkiRNsxrq72tpanDhxAjk5\nOTh16hQOHz6MtLQ0AHeHSERGRiIyMhKenp6IjIxEaGgoHBwcRI6adSbV1dVITExESkoKcnJykJiY\niMTEROEqpL+/P6KiohAdHQ1/f3+MHj2ax4p3Azk5OcjNzcXhw4dx+PBhnDlzBpWVlQCAQYMGITIy\nEl5eXkIf4u3t3WxIBet5lEqlVj+h7ivS0tJgaWmJkJAQ+Pv7Iy4uDlFRUQgODhY75PbACTZjXV1a\nWhr27t2Lffv2IS0tDWlpaZDL5YiLi4Onpyeio6MRFRUFLy8vsUNlPYBSqcThw4eRmJiIw4cPC0ma\nv7+/8KE6ceLE7vqh2qUkJibi+++/R1paGvbt24fq6mrY2NggOjoacXFxiIyMFP7fGGsP6gQ8LS1N\n6DcACH3FjBkzuurfICfYjHV2jY2N2L17N7Zu3YpLly4hNTUVXl5eGD9+PAYMGIBx48ZhyJAhYofJ\nmNF+//13/Pzzz0hNTcX+/ftRVlaG++67DyEhIXjyyScRFxcndojdQm1tLbZt24bLly/ju+++g0Kh\nwKBBgzBw4EBMmDABEydOhIeHh9hhMqZXXl4ebty4gZ9//hn79u0ThiPFxcXh6aefxhNPPNFZn2rL\nCTZjncXly5exdu1aJCcn4+jRo3B3d8dTTz2FwYMH44knnuC5XVmPkJ+fjy1btmDLli1ISUlBVVUV\nJk+ejMGDB+P5559H7969xQ6x07lw4QL++OMPbNq0CadOnYJEIsHkyZPx9NNPY+rUqc1mz2GsO2hs\nbMTOnTtx6dIlbNmyBWlpaZDJZHjyyScRHx+PsLAwMcPjBJsxMWVlZeHs2bNYtWoVjh8/jmHDhiEk\nJARPPPEEHnzwQbHDY0xUKpUKP/30E7Zu3YrLly/j2rVrePTRR7F48WJERkb2+HsFdu3ahS+++ALH\njx+Ho6MjZs6ciaeffhpDhgzp8e8N63nOnDmDS5cuYfPmzThx4gRiYmIwfPhwvPTSS2IMkeQEm7GO\nkp2djffeew9nzpzBlStXOFFg7B41PTEdNWoURo0ahTfeeAP29vZih2dySqUSCQkJ+PDDD3Hu3DlM\nmjQJw4cPx6JFi7jvYKwFv/32G1auXIm9e/fC0tISL7zwAt555532fl6AcfPREpEYP4x1aSdOnKDI\nyEiysLCgyMhI2rNnj9ghdSm4e5LdpjbefPNNMjc3b3M7upiyzYsXL1JQUBBZW1vTqFGjTNZuT7Nj\nxw7hmIuKiqJz586JHdI9uXXrFq1cuZKsra3J39+fduzYQYWFhWKH1emZos+orq4mf3//dusz2qNd\nIiJzc/N2abe7OnToEEVGRpKZmRlFRkbSuXPnSKVSmXITRuW6PWLCQsZMZe3atZgwYQJGjRqFmJgY\nlJWVCVedWMf66KOP9D76XG3UqFEdFI1+oaGh2Lp1q/AEx3/+859ih9QlzZgxA+fOncM333wDHx8f\n3HfffZg3bx727dsndmhGqaurw4IFC9CvXz989NFH+Pbbb3Hr1i3MmDGD5xo3Apng2/Z33nkHzz77\nLA4ePGhwvXvpN0wRny7V1dU6n/zK9IuLi8O5c+eQm5uLgIAA3HffffD29m7x/93UeIgIYwYUFRXh\nxx9/xIIFCzBz5kx8+eWXkEqlYofV5amf+NiW/kcikbTbh1p7tV1VVQUPD48WTwyY8QoKCvDss8/i\n0KFDWLt2LaZNm9ZphpKcPHkSEyZMwJAhQzBp0iQsXbpU7JC6tLYel+bm5u2arJq63zh+/DhmzpyJ\nrKysduvrepKMjAwsXLgQR44cwcSJE7Fly5Z7ndPfqCEifAWbsSZu3LiB1157DZaWljh27Bhmz54N\nlUqFjRs3cnLdTp5//nn06dMHvXv3Fp4UBgCHDx+GXC5HREQEtm3bJryuTtBbejS7ZrlEIoFEIkFm\nZiYcHBwwc+ZMrXXHjx8PmUyGRx99VGd8VlZWWvGp2wOAFStWaC3rUlpaitDQUCQnJxuMmbWOq6sr\n9u7di5qaGowZMwZr166FRCLBV199JUo8e/fuhbm5ufCI8NLSUpw8eZKTaxPLz89Hnz59YGVlhfnz\n52uVTZ48GTY2NoiIiBBek0gkUKlUkEgkWLZsmd52mx7H6j7jkUcegbu7u/BEXABITk6GVCqFTCZD\nRkaGzvh69+4txKduW7P/MtRnbN68GZmZmUa8G8wYPj4+2Lt3L6qqqrBjxw689957MDc3x759+9rn\nxMvYsSQm/mGs0/n1118JAO3evZsaGhrEDqdbg8Z4xfj4eFq3bp1WWdPfr1692mx8Y9Nlfdtpunzs\n2LFmZTNnzqRNmzbprGcovueff5569+5N/fv319rO8OHDm8VSW1tL7777Lj3zzDMtxs3aTqlUkqen\nJ1lbW9Pt27fbdVu1tbW0atUqsrKyohs3brTrtnoyXX0DEdHOnTvpjTfeEJbV/99Xr17Vuu/BmD5D\n13bUfcbt27fJy8tL53q7du0yKr7nn3+eAFBycjJt2LBBa7sjR44Ufvf29m513Oze3Lhxg+bNm0d9\n+vSh2tpaY6oYletygs0YEV2+fFmrI2XtSzPB9vLyopycHK2yphoaGkyWYJeVlTUrc3d3p+zsbJ31\nDMVXV1dHAGj58uUtxtKauJlp1NTU0Ndff002NjZUWlrabtvp378/2draUmZmZrttg+lPsAsLC2nI\nkCHN1m9oaCAXFxeddVqzHXWfUVtbSxKJROd6BQUFRsVXV1dHo0aNIktLS4MxvPzyy62Om7XNSy+9\n1OxiiR58kyNjLTl9+jRcXV2RmJgIIkJsbKzYIfU4EolE62ldpDHWUCKRoLi42KQP02jt1GeG4rO0\ntIRUKsWePXtMFh8zHWtra8THx6O6uhpvvfUWpk2bhoKCApO1v3HjRoSGhiI1NRVVVVXo06ePydpm\nxnNxccGlS5cAAA899BDeffddAHfHXGsO6bhX6j7DyspK71hoQ2N5NeOztLREVFQU6uvrUVlZqbfO\n559/3oaI2b1YuXIlUlNTERoaCplM1ub2OMFmPdarr76K06dPo6CgAHPmzBE7nB7r4sWLCA4Oxg8/\n/IDy8nL4+/trlZuZmeGVV15p1xhOnz6NoUOHoqKiAqdPn9YZX1FRkVZ8CQkJcHd3R3l5Oe7cuaM1\n5jcqKkr4XSKR4Pjx4ygvL8e2bdtga2vbrvvCdFu1ahX++9//YubMmTh06FCb2qqursbkyZPh4uKC\nixcvmihC1hoDBgzADz/8gKKiIuzbtw/Hjx8HcLe/qKurQ3Fxcbv3G05OTvjtt99QUVGBRx55RGd8\n5eXlWvG5u7vjmWeeQUpKCqRSqVa/ER0d3a7xMuNcvHgRpaWlkEqlqKqquud2eBYR1iPt3r0bSqUS\nc+fOFTsUxlgHmzhxItatWwd3d/d7qm9lZYUjR450imkgGWPtx8rKCh98XZC8WAAAFJZJREFU8EHT\nm5T5SY6M6VJXV4d+/frx3dmM9VAlJSV444038OWXX7a6rkKhwIYNG3hWEMZ6gM8++wxvv/02qqur\nNV82KsG+pwkAGevKZs6ciaFDh4odBjMRfdNciXTxgHUBTk5O+PbbbxEbG4vHH3+8VXUnTZqEs2fP\ntlNkrKNwv8GM8dprryE/P/+e6vIYbNbj3L59G/379xc7DGYi+u7gZswQb29v3L59u9X17ty5Y/pg\nWIfjfoMZq1+/fvdUjxNs1uO8/fbbXfJx1S09VKUlS5cuxUcffYRff/21zW01Zer2NP3vf//D4sWL\nUVhYiMWLF+O7774zGEdhYSEKCwthZmZmVJlEIsHhw4dRWlqKPn36aM0YovlgCPXPyJEjAQC//vor\nQkNDde67oXo3btzA2LFjceHCBVy7dk2rfnR0NKqrqxEVFdWsXV1ttlRWWlqKsLAwrFmzBnl5ebC1\ntcWKFSuEeqGhoYiJidG5D/rK1Df/KBQKoc2uqKSkBC+++GKr67311lsoLCxsh4jaT1uPT4lEIvQd\nptQe/Yb6OF+8eHGzPqDpeup+xczMTKtfaVqm+frFixdx5coVREVFaX2OaPYjP/30k1Y/YiguzeNp\n9erVOo+n6upqo/oDdR9jY2ODPXv2QKlUYu/evVptGirT178Ad/s7fX2FvjJd78mRI0ea1e/MiouL\n8frrr99bZWPn8zPxD2OiUalUFBIS0uUeJoM2zoXq6+tLRUVFJopGW1tjM8Te3p5UKhUR3f2/c3Bw\noLy8vGbr5eTk0IgRI4Tl4cOHC+sZKtOM/cCBA3rnslVTP5AmICCg2cMl9NUbP368UG/IkCFaDzPQ\nXFf9kJIrV660OO/3+PHjWyx76aWXtMref/99Mjc3F5Z37dqldz/1lb300kv02WefabV55cqVZvU7\ns5s3b9J77713T3UrKipo/vz5Jo6ofbX1+ATQLn2HqfsNzeNcpVJpHee61lP3K8OHDycHBwe9Zbr6\niitXrlDv3r317ovmsqG4jDmemh7Huran2ccYWrc1ZZoCAgL0rqOvTNdyaGio3m10RgsWLCBnZ+em\nLxuV63KCzXosiUQidKJdgSk+JNtLR7YNgEaPHt1svdjYWLp06ZKwfPHiRWE9Q2Wa0tLSDO7La6+9\n1mJ8uixdurRV6yckJLS4nmabml577TWhzMLCQqud6upqo04IDJVZWFhQdXW1Vpvh4eEGY+1M8vPz\nycPDo01tvPHGG2RhYWGiiNpfZ+07TN2usce5rvXUsRjbRkJCAk2aNElvLJr7ZqjNlo6n8vJy+uWX\nX1rVH7RHgm3MOi3VB6DzKbed1f/+9z9atmyZriJOsBlriYuLC50+fVrsMIyi2Xn5+vqSu7s7lZeX\nk6+vL/Xt21comzJlChUUFNCYMWPowIEDOuvrM23aNK31PvjgA5ozZ45Qv6CggNLT0++pA8/LyyNf\nX186ePAgJSQkkK+vLymVSiK6+4jgqKioFvdbvWxtbd1sPSsrK6qvrxeW6+vrhfUMlWmytbXVeuSy\npg0bNmhdNdYXn656Tdf/9ttvSaFQ0Jdffqn1SGR1+dSpU+ny5ctGt6lJM8bnnntOK77PP/+8zQn2\nc889p3XF7fPPP+8SyWZjYyPZ2NiY9APe0tKySzy9sWnfoesYBEBTpkxp8fjWZ9q0aVrJyAcffKBV\n35g+SV8/kpeXJ/R3CQkJWv2d5uPFjT3Oda2n3l5LbXzyySfCMarP+fPntfoRQ20aOp72798v7Kuh\n/4Om/UFJSYlwcm1ubk4lJSVGlTk4OFBxcTGtX7+epFKp1tNt1e41wW76nnRmmZmZ9Je//IW++OIL\nfatwgs1YS7Kzs8nc3JwWL14sdigtavrhs27dOiIi2rlzp1bZ7du3iYjo6tWrNGrUKJ319Tl79iwB\nEB4r7e7uTsnJyTq3byg2XWXx8fFaZTt37hQ63OHDh2t9UOprW73cHgn2hQsX6JVXXtEZAxHR/fff\nT/v27WsxPl31DK3fdLm2tpbeffddsrW1paqqKqPa1KQZY1ZWFvXu3ZvWrFlD+fn5NHDgwDYn2FlZ\nWWRvb08KhUJo09HRUW/9zmLUqFH0wQcfaP0dtNW5c+dILpebtM32oO/41DwGAQh9x70k2GfPniWZ\nTEZERFVVVeTu7t6sfkt9kr444+Pjhf6uaZnmCVNHJNhEd4dyvfvuu/TMM880a/vChQvk5ubW4vbU\nbWoeT998843W8eTt7U3Hjh1rts9NNe0PwsLCaOPGjVRQUEAbN27UuiJuqEzTv//9b3ruueeavX6v\nCXbT96Szqq+vJ7lcToGBgYZW4wSbMWNVVFSQra0tvfPOO2KHolfTD5+mP0RESqWSAgMDSSqVar3e\ntL4hBw8eJA8PD2poaNC6ur98+XKd7eqKTVeZ+qqJ5o+dnV2r9lu9bOohIpmZmVpX3HRZsWKFUfG1\nVM/Y5CUrK0vX2D+aO3eu3ljmzp1rMBZdY7sNxdBSmbrNJ554wuA6YklLS6PFixeTt7d3uw4HmzFj\nBgGgPXv2tNs22sJQ36E+BpcvX06BgYEtHt8tbaempob8/f2FvkOpVOrtO4ztR3T1Hbp05BARXfHr\n60da06bm8aSrn9e1763pY4z9v62uriZLS8tmr7e2r8jMzGz2LV1nNXHiRIqLizNmVU6wGWutgoIC\ncnV1JQsLC62vMjuDpp3kzp07heWEhAQiIrKzs6OysjIiIqqpqbmnBFu9blhYmMHt6yvz9PSk9PR0\nIiI6evSoULZw4cJm9YYNG9ZiLPb29lrLUqmUcnNzm62XnZ2tdRV85MiRwnqGympqalpMrv/1r3/p\nLTP0vuqqp3mTYdP6V69eFX7funWr1tU+taefflrv9nSVabY/ePBgnTcZtfZD8/r161ptdrYbhr/6\n6isCQFu2bOnQ2M6dO0fm5ub097//vVMNHTF07KqPQQBC33GvCfa2bduof//+9PPPPwuv2dnZCfVb\n6pP0lS1cuFBnf9eUoeO8pfWkUmmLbRiK11A/0lJc6uOptrZW7/Gk7/9AVx9zrwm25nH90Ucf0YIF\nC4yOQ1eZrvekNZ9DHeHvf/87mZub07Rp01pTjRNsxtoiOTmZ+vTpQwC0xsiJoenVi+LiYlqyZAlZ\nWlrSpEmT6MyZM0REpFAoyM3NjaysrGj79u06r1wZ46uvvqLvvvtO67VZs2aRlZUVDR48WKutpu2q\nT1ImTZpEGRkZWmXFxcXUt29fcnd317pByNAQEaK7V4CkUmmzqz5N9+fgwYMklUpJKpXSoUOHjCoz\n5gqRvjvfDdU5cOCA3nrR0dEklUopKCiIysvLhdfj4+PJxsaGfH19hbHvTdvUR1/Zb7/9Ri4uLtS/\nf38qLCzUG7+uD159ZVOnTiVra2udbYqhsbGRFi1aRABo2LBhVFxcLGo8tbW1tH37drKwsKA5c+ZQ\nXV2daLE0/f/TdwzOmjWL3Nzc9B7frbmKrUmhUAh9h7pP0td3PPXUU2Rvb0+TJk1qVqbu79zd3YX+\njoiajalXH+ejR4/WeZxrrqfuV3T1FbrKzpw5Q4GBgWRlZUW+vr6UkpLSrH1975e+uIj+73hasmSJ\nzuPJULu6+pibN2+Sv78/WVlZkb+/P926dcuoshdeeIFsbGwoMDBQ503Uhv4WdJUZewW+I9XV1dGc\nOXPIwcGBxo8frzWjUysYlevyo9IZM0ClUuH06dOYOnUqXF1dsWbNGp1zFDPGOl5NTQ1OnjyJOXPm\nQCaTYffu3Rg4cKDYYQmKi4sxefJkpKSkIC4uDuvWrYODg4PYYTHWI+3duxd//etf0atXL8yePRtL\nly6916aMSgD4QTOMGWBmZobo6Gjk5+cjOTkZNjY2ePvtt2Fubo7Y2FicPXsWjY2NYofJWI9QVlaG\nRYsWISAgAFKpFOvXr0dQUBCys7ORkpLSqZJrAHB2dsbJkydRXFyMHTt2YOvWrZBKpZBIJPjb3/6G\nyspKsUNkrFuqrKzEgQMHEBgYCKlUismTJ+P++++HQqFAcnJyW5Jro/EVbMbaICUlBceOHcP777+P\nqqoqjBgxAosWLcLEiRPFDo2xLo2IsGnTJqxatQoXLlxAYGAg3n33XUyfPl3s0Exmy5YtePvtt3Hr\n1i2MGjUKL7/8MqZOnSp2WIx1OTt37sTvv/+O//znP+jduzfef/99PPDAA/D09GyPzRl1BZsTbMZM\nTKlUYvXq1Vi9ejXy8/MRFhaGxYsXY/bs2TA3Nxc7PMY6ncrKSnz99ddYtWoVMjMzERAQgBdffBHz\n58+HpaWl2OF1mPLycqxevRp//PEHfvrpJ0REROCll17CsGHDEBAQIHZ4jIlOfeL9xRdf4NKlS3Bx\nccHixYuxaNGijhx+xQk2Y2LLycnB2rVr8dlnn6G+vh4DBgzAjBkzMHv2bPj7+4sdHmOiaGxsRHp6\nOr7//nts3LgR169fh4ODA1566SW8+OKL6NWrl9ghii4nJwerVq3CqlWrUF1djUGDBmH+/PkIDg5G\nXFyc2OEx1mHKysqwe/duJCYmYv369aipqcHcuXMRFRWFWbNmiXFPFCfYjHVWv/32G7Zu3YpLly7h\n7Nmz8Pf3x5AhQzBlyhRMmDABrq6uYofIWJulpaVh//79+PHHH3Hp0iUUFRVhzJgxeOqppzBkyBBE\nRkaKHWKXk5mZic2bN2Pz5s24cuUKrK2t8cQTT+CRRx7B+PHjYW1tLXaIjLVabW0t9u/fjxs3bmD7\n9u1ISkrC4MGDMWTIEMycORPjx48XO0RNnGAz1tWoVCr89ttv2L9/P1JTU3Hw4EHU1dVh+PDhGDBg\nAMaPH48HHngA7u7uYofKerj6+npkZWXh8OHDwt/rlStXIJPJMH78ePTv3x8TJkzAsGHDxA61R6ip\nqcHPP/+M1NRU7Nu3D6dOnYKVlRX8/f0xceJEjB8/Hn5+fujbt6/YobIeqL6+HidPnkRqaip++eUX\n7N+/H3V1dXB1dcXEiRMxYcIEjB8/Hra2tmKHagxOsBnrburr63Hq1CkkJCQgIyMDJ0+exPXr1wEA\nXl5eiImJQVRUFHx9fXH//fdzIs7uSWZmJk6fPo2MjAycOHECJ06cQElJCQAgLCwMMTExiI6OFv7O\nWOenUChw584dnDhxAsePH0dCQgLKysoAAEOHDkVMTAx8fHwQGxuLkJAQnoqUGYWIkJ+fj7Nnz+L4\n8eM4ceIE0tPToVAo4OjoiJiYGPj5+WHUqFGIjY3tLp9JnGAz1hMolUqkpaUhMTERhw8fRmJiIm7f\nvg0iglwuR2RkJDw9PREZGYnIyEiEhYVBKpWKHTbrBHJycpCbm4vExEQkJiYiJycHiYmJyM3NBQDI\n5XLExcUhMjIScXFx6N+/P2QymchRM1Oprq7GqVOncPjwYZw8eRK5ublIS0sDAAwaNAheXl5Cv+Hp\n6Ynhw4fDwsJC5KiZWJRKpVY/kZiYiLNnz6K+vh7A3b+Z6Oho+Pv7C/1GN8UJNmNMW3p6Os6fP4/s\n7GycP38eFy5cQG5uLnJycgAA1tbW6NWrFwYOHIigoCAEBATAxcUFgYGBCAwMhL29vch7wHQpKirC\n9evXcePGDVy9ehWFhYXCclFREVQqFQCgX79+8PDwQEREBCIiIhAeHg53d3d4eHiIvAesM6uqqkJ2\ndjaSkpKEn9zcXCQnJwvryGQyoc8ICgqCq6srAgICEBAQwDetdjJFRUVCH3H16lVcu3ZNWE5NTRXW\n69WrF8LCwhAREQEvLy9EREQgMjISdnZ2IkbfKXCCzRgzjVu3bgkJW2lpKTIzM5Geni78npeXp7W+\nh4cHvL29IZPJ4ObmBg8PD7i5ucHGxgbu7u7o1asX7Ozs4O7uDmdnZ8jlcpH2rGMplUrk5eVBoVCg\nrq5O+L2oqAiVlZVQKBQoLCwU3tuioiKt+j4+PvD29oajoyO8vb2FMbXqZKZPnz4i7RljzWVnZ+P6\n9evIz89HWloa0tPTkZ6ejrKyMmRkZCA7O1trfU9PT+Hv28PDAx4eHrC2toaHhwfc3d0hl8shk8ng\n4uICZ2dnODk5ibRnHaO0tBS1tbXIy8tDfn4+lEolSkpKUFxcjMLCQpSVlUGpVCI9PR2ZmZkoLS1F\nVVWVUF8qlQp9hq+vL5ycnODj44PAwEChz+CbYu8JJ9iMsY5RX1+PzMxMKJVKZGZmCj+lpaXIz8+H\nQqGAQqEQPiyaPv1SIpHAyckJcrlc+AHuDlGQSqWwtLSEk5MTLCws4OjoCBsbG9ja2kIul8PJyUlr\nvKh6fU12dnYtfpBUVFQIX3WqKZVKreW6ujpUVlZCqVSitLQUKpUKSqUStbW1qKqqQmVlJerq6qBU\nKoW6SqUSBQUFKC8vb7ZNW1tbuLu7w8XFBfb29nB3d4erqyt8fHzg5OQEb29vIeHw9fVt4X+Bsa5F\nPV1jSUkJMjMzkZGRgYyMDJSVlSEvLw95eXlCn6FQKJrVV/cDbm5ukEqlwrHftA9xcnKCpaUlHBwc\nYGtrCxsbm2Yn9Q4ODs2Gv7R04t/Q0NDsuG7aZ2j2A+Xl5aivr0dJSYnQl6j7Hc0+o6ioCHl5eaip\nqWm2TScnJzg7O6NXr15wdHSEXC6Hr68vfHx8IJPJhGTa19eXn7vQfjjBZox1f/o+0DSVlJSgpb7O\n2tq62VefTT9gLSwsOvJhBoyxdqLZTxCRcBOvmkqlQmlpabPXzMzMtF5r2kc0PeHvKd/O9TCcYDPG\nGGOMMWZCRiXYZi2vwhhjjDHGGDOWWPPt8ASbjDHGGGOsW+Ir2IwxxhhjjJkQJ9iMMcYYY4yZECfY\njDHGGGOMmRAn2IwxxhhjjJkQJ9iMMcYYY4yZECfYjDHGGGOMmRAn2IwxxhhjjJkQJ9iMMcYYY4yZ\nECfYjDHGGGOMmRAn2IwxxhhjjJkQJ9iMMcYYY4yZECfYjDHGGGOMmRAn2IwxxhhjjJkQJ9iMMcYY\nY4yZECfYjDHGGGOMmRAn2IwxxhhjjJkQJ9iMMcYYY4yZECfYjDHGGGOMmRAn2IwxxhhjjJkQJ9iM\nMcYYY4yZECfYjDHGGGOMmdD/ByQwjDkX+SwUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84eca96a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "\n",
    "try:\n",
    "    import matplotlib.pyplot as plt\n",
    "except ImportError:\n",
    "    raise ImportError('You need to install matplotlib for plotting.')\n",
    "\n",
    "# 加载数据集\n",
    "print('加载数据...')\n",
    "df_train = pd.read_csv('./data/regression.train.txt', header=None, sep='\\t')\n",
    "df_test = pd.read_csv('./data/regression.test.txt', header=None, sep='\\t')\n",
    "\n",
    "# 取出特征和标签\n",
    "y_train = df_train[0].values\n",
    "y_test = df_test[0].values\n",
    "X_train = df_train.drop(0, axis=1).values\n",
    "X_test = df_test.drop(0, axis=1).values\n",
    "\n",
    "# 构建lgb中的Dataset数据格式\n",
    "lgb_train = lgb.Dataset(X_train, y_train)\n",
    "lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
    "\n",
    "# 设定参数\n",
    "params = {\n",
    "    'num_leaves': 5,\n",
    "    'metric': ('l1', 'l2'),\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "evals_result = {}  # to record eval results for plotting\n",
    "\n",
    "print('开始训练...')\n",
    "# 训练\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=100,\n",
    "                valid_sets=[lgb_train, lgb_test],\n",
    "                feature_name=['f' + str(i + 1) for i in range(28)],\n",
    "                categorical_feature=[21],\n",
    "                evals_result=evals_result,\n",
    "                verbose_eval=10)\n",
    "\n",
    "print('在训练过程中绘图...')\n",
    "ax = lgb.plot_metric(evals_result, metric='l1')\n",
    "plt.show()\n",
    "\n",
    "print('画出特征重要度...')\n",
    "ax = lgb.plot_importance(gbm, max_num_features=10)\n",
    "plt.show()\n",
    "\n",
    "print('画出第84颗树...')\n",
    "ax = lgb.plot_tree(gbm, tree_index=83, figsize=(20, 8), show_info=['split_gain'])\n",
    "plt.show()\n",
    "\n",
    "#print('用graphviz画出第84颗树...')\n",
    "#graph = lgb.create_tree_digraph(gbm, tree_index=83, name='Tree84')\n",
    "#graph.render(view=True)"
   ]
  },
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   "source": []
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